| Hu Zhentao (胡振涛),Hu Yumei,Zheng Shanshan,Li Xian,Guo Zhen.[J].高技术通讯(英文),2016,22(2):142~147 |
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| Distributed cubature Kalman filter based on observation bootstrap sampling |
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| DOI：10.3772/j.issn.1006-6748.2016.02.005 |
| 中文关键词: |
| 英文关键词: state estimation, cubature Kalman filter (CKF), observation bootstrap sampling, distributed weighted fusion |
| 基金项目: |
| Author Name | Affiliation | | Hu Zhentao (胡振涛) | | | Hu Yumei | | | Zheng Shanshan | | | Li Xian | | | Guo Zhen | |
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| 中文摘要: |
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| 英文摘要: |
| Aiming at the adverse effect caused by observation noise on system state estimation precision, a novel distributed cubature Kalman filter (CKF) based on observation bootstrap sampling is proposed. Firstly, combining with the extraction and utilization of the latest observation information and the prior statistical information from observation noise modeling, an observation bootstrap sampling strategy is designed. The objective is to deal with the adverse influence of observation uncertainty by increasing observations information. Secondly, the strategy is dynamically introduced into the cubature Kalman filter, and the distributed fusion framework of filtering realization is constructed. Better filtering precision is obtained by promoting observation reliability without increasing the hardware cost of observation system. Theory analysis and simulation results show the proposed algorithm feasibility and effectiveness. |
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